Conventionally, a method for recognizing positions on a surface of a wall (planar obstacle) is known from the document (Li Zhang & Bijoy K. Ghosh, “Line Segment Based Map Building and Localization Using 2D Laser Range finder”, IEEE Int. Conf. On Robotics & Automation, pp. 2538-2543, 2000). In the conventional method, a horizontal plane is scanned at an every constant angle or a constant distance with using a laser radar so that a group of scanning points each distributed in two-dimensional surface is acquired by receiving reflected waves from an object. A plurality scanning points are selected with specific assumptions from the group of the scanning points, and an aggregation of segments is formed by coupling the selected scanning points. The wall distributed in the horizontal plane can be recognized by the aggregation of segments.
In the above mentioned conventional method for recognizing the planar obstacle, it, however, includes a step repeating a calculation for acquiring a distance from a specific point to a specific segment in a plurality of times when specific scanning points are selected among a lot of the scanning points and the aggregation of segments is formed. Thus, the calculation process becomes complex and requires a long time. If the method is installed in the autonomous vehicle for recognizing the planar obstacle, the moving speed of the autonomous vehicle may be slowed so as to autonomously move the autonomous vehicle safely and surely. Therefore, it is necessary to simplify the planar obstacle recognition method so as to be installed in the autonomous vehicle. In addition, according to the conventional planar obstacle recognition method, when the planar obstacle has apertures like a meshed or grated fence, reflected waves from an object disposed behind the planar obstacle are received, so that it is difficult to recognize the planar obstacle having apertures, accurately. Therefore, no method which can recognize the planar obstacle having apertures is put into practical use as the planar obstacle recognition method which is suitable for autonomous vehicle.